The OSI-906-Performance

4 The guideline includes scoring 2000 flupentixol cells to accurately estimate MN frequency. Because these guidelines were developed for assay performance, they do not address how to statistically analyze the data generated by the assay. This has led to the application of various statistical methods that may render different interpretations and conclusions. In a review article examining analytical methods, Ceppi et al.5 reviewed 63 studies that statistically analyzed MN data and developed recommendations for selecting an appropriate analytical method. The review included studies that applied both parametric and nonparametric tests. The nonparametric tests included Kruskal�CWallis, Friedman, Wilcoxon, and Mann�CWhitney U-tests. Although these tests do not require an underlying distributional assumption, they are unable to adjust for confounding factors. There were a variety of parametric Selleck Lapatinib tests performed that assume normality, such as analysis of variance, analysis of covariance, and multivariable linear models, which can adjust for confounding factors. Other methods such as correlations and Student��s t-test were also used. However, applying these methods to MN data, which are rarely normally distributed, could result in inappropriate inferences. Although the data could be transformed to better adhere to a Gaussian distribution before applying such parametric tests, few studies applied any type of transformation. Further, Student��s t-tests and Pearson��s correlation cannot adjust for confounding variables. The common non-Gaussian models used were log-linear, Poisson, negative binomial, OSI906 and logistic regressions. The logistic and log-linear models account for categories, whereas Poisson and negative binomial models directly model count data. For this reason, Ceppi et al.5 recommend using negative binomial or Poisson models for MN data analysis. Another advantage of these count models is that they can adjust for confounding variables such as age, gender, and smoking status. Finally, Ceppi et al.5 recommended that 2000 or more cells be scored for best model performance. If